Fiber Review

Fiber (fiber.ai) has repositioned since its earlier incarnation as a self-serve lead database. The current product is an "agent-native" B2B data API: company and people search, contact enrichment, and audience-building workflows exposed through a REST API, SDKs, webhooks, and public MCP servers meant to be called directly by coding agents like Cursor or Claude Code, not just browsed in a dashboard. This review covers what's verifiable on the live site today, including a notable gap: no public self-serve pricing page.

Verdict at a glance

What kind of tool is Fiber?

Fiber is B2B data infrastructure delivered as an API rather than a search-and-export SaaS app. It resolves companies and people from structured filters (industry, headcount, funding, geography, tech stack) or natural-language queries, then enriches matched records with work emails, personal emails, phone numbers, and live LinkedIn profile fields. The company describes itself as Y Combinator-backed (S23) and says it serves "100s of GTM and recruiting teams," plus AI-native products that embed Fiber as their underlying data layer.

What's notably different from a typical prospecting tool: Fiber's own documentation frames the primary user as an LLM agent, not a human clicking through a UI. The docs instruct calling agents to poll rate limits no tighter than every 30 seconds and to confirm cost estimates before running expensive operations — language written for automated callers, not sales reps.

How it works

Access runs through a REST API (documented at api.fiber.ai/docs), official TypeScript (@fiberai/sdk) and Python (fiberai) packages, and public MCP servers at three tiers (V2, V3, Core) intended for direct use inside coding-agent tools. Typed filters (canonicalized company slugs, structured seniority levels) sit alongside a natural-language search mode that translates freeform queries into those same filters. Webhooks push JSON notifications when a tracked company or person record changes, and there's an in-app dashboard for browsing and export for anyone who wants a UI rather than raw API calls.

The cost-control mechanism is built into the API itself: an estimateEnrichmentCost endpoint and a free credit-balance check let a caller (human or agent) preview spend before committing, and the API returns a 402 status with a purchase link when credits run out.

Fiber pricing (2026)

Fiber does not publish a pricing or plans page — it's absent from the site's sitemap, and dedicated URLs we checked (/pricing, /pricing-plans, /buy-credits) all 404. What is published is a credit-consumption table for individual operations:

The documentation itself cautions that "actual per-org pricing may differ; always trust the live estimate" — meaning even the credit-to-dollar rate can vary by account, and there's no way to compute a real all-in cost without signing up and pulling a live quote. Treat any third-party estimate of Fiber's price (including ours would be, if we guessed) as unreliable until you see your own account's numbers.

What we could and couldn't verify

We could verify: the API/SDK/MCP architecture, the credit-cost table above, the Y Combinator (S23) backing claim, and the absence of a public pricing page — all confirmed directly on fiber.ai and its documentation subdomain.

We could not verify: actual dollar pricing, accuracy or bounce-rate figures for the enrichment data, or any independent user sentiment. We checked G2 and Trustpilot listings for Fiber and both returned access-blocked responses rather than review content, and no Reddit or forum discussion of the product surfaced in available research. That doesn't mean the product is unproven — it means there's no public third-party evidence to report either way, and we're not going to invent a rating to fill the gap. Worth noting separately: older marketing copy associated with this domain described a more traditional lead-database product with a stated "0% bounce guarantee" and flat monthly plans; none of that language or those guarantees appear on the current live site, which now reads as a materially different, API-first product.

Pros and cons

Pros

• API-first design fits agent and developer workflows better than dashboard-only competitors
• Built-in cost transparency: estimate and confirm before every charge, rather than surprise credit burn
• Live LinkedIn data and 44+ enrichment fields returned without manual field selection, per vendor docs
• Public MCP servers and SDKs lower the integration lift for technical teams

Cons

• No public pricing page — you cannot compare real cost against competitors without creating an account
• No independent reviews found on G2, Trustpilot, or Reddit at time of research
• Non-technical teams that want a point-and-click prospecting UI are not the primary audience here
• The product's positioning has shifted noticeably from earlier marketing, which makes it hard to know how mature the current feature set is in practice

Fiber alternatives

Fiber competes in B2B contact data and enrichment, a crowded category with both API-first and dashboard-first options:

• Apollo — the most commonly cited comparison; a full prospecting dashboard plus API, with published self-serve pricing.
• Clay — enrichment and workflow-automation platform that layers multiple data providers (including API-first ones like Fiber) behind a spreadsheet-style UI.
• ZoomInfo — the incumbent enterprise data provider; deeper data depth, higher price, sales-led contracts.
• People Data Labs — a comparable API-first, developer-oriented B2B/person data provider with published usage pricing.
• Cognism — dashboard-based B2B contact data with a compliance-forward pitch, mainly for EU/UK-heavy teams.

Who should use Fiber — and who shouldn't

Good fit: developers and technical GTM/recruiting teams building an agent, internal tool, or automated workflow that needs programmatic company/people data with cost controls baked in; AI SDR or recruiting-automation products that want to embed a data layer rather than build one.

Poor fit: teams that want to see pricing before signing up; anyone who needs independent proof of data accuracy or deliverability before committing budget, since none is currently public; non-technical users who just want a search box and an export button without touching an API.